Reported impact
Business outcome
IBM reduced legacy data costs, lowered redundancy and created a single governed data foundation that supports large-scale operational analytics and AI use cases.
- Compliance
- Cost Reduction
- Scalability
IBMSoftware / SaaSData Lake / Data Warehouse ModernizationData AnalyticsData PlatformComplianceCost ReductionScalability2025 IBMIBM#0f62feSoftware / SaaSGlobalIBM’s CIO organization needed to collapse multiple operational data lakes into a single governed platform so teams could access more data, reduce duplication and standardize controls across infrastructure, application…1.2B+ rows loaded over 4 daysSep 25, 20256Data Lake / Data Warehouse ModernizationIBM Cognos Analytics · IBM Knowledge Catalog · IBM watsonx.ai0 2025-09-25 6
IBM
Data Lake / Data Warehouse Modernization
IBM’s CIO organization needed to collapse multiple operational data lakes into a single governed platform so teams could access more data, reduce duplication and standardize controls across infrastructure, application and enterprise-tool data.
1.2B+ rows loaded over 4 days
26% reduction in data redundancy for Identity
7.7% reduction in data redundancy for Developer Experience
USD 5.3 million in savings
Solution
The IBM CIO organization implemented an I&T Data Platform centered on IBM watsonx.data to consolidate fragmented operational IT data. Before the program, the organization stored information about applications, devices, networking, infrastructure and enterprise tools across seven different data lakes, each with inconsistent governance and overlapping ETL. The target platform combined IBM watsonx.data, IBM watsonx.ai, IBM Cognos Analytics and IBM Knowledge Catalog to create a unified hub for ingestion, governance, analytics and AI-driven insight. Data is loaded from legacy sources into the centralized platform, cataloged for controlled access, analyzed through Cognos dashboards and explored with watsonx.ai-driven machine learning. By replacing isolated repositories with one shared environment, the CIO team reduced data redundancy, lowered legacy costs and created a single source of truth that can scale further as new operational domains are onboarded.
Data flow
Operational IT data from legacy systems is ingested into watsonx.data, cataloged and governed through Knowledge Catalog, analyzed with watsonx.ai and surfaced through Cognos Analytics for self-service decision-making and reporting.
Solution architecture
- Compute
- IBM watsonx.ai Applies machine learning and AI-driven analysis to the consolidated operational data.
- Storage
- IBM watsonx.data Serves as the central data platform that consolidates operational data from legacy lakes.
- Serving
- IBM Cognos Analytics Delivers reporting and dashboards over the unified operational data set.
- Governance
- IBM Knowledge Catalog Provides governance and controlled access to the centralized data assets.
Architecture clues
- 100% SaaS deployment in IBM Cloud for watsonx.data
- Additional 229 GB ingested daily from devices and intranet use cases
- Platform replaced seven different data lakes
Evidence from the source
The platform has enabled USD 5.3 million in cost savings by Q1 2025.
The team successfully loaded more than 1.25 billion rows from IBM’s intranet, demonstrating the platform’s capability for efficient, large-scale data ingestion.
implementing the I&T Data Platform powered by IBM® watsonx.data®, alongside IBM watsonx.ai®, IBM Cognos® Analytics and IBM Knowledge Catalog.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value100/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/254 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence84%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence15/203 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.ibm.com/case-studies/ibm-cio-watsonxIBMFirst-party
IBM customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Sep 25, 2025 · IBM · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
Related
Similar use cases
Snowflake Sample Modified · Sep 11, 2026 Order.co replace a warehouse where development workloads, production pipelines, ad hoc analytics, and real-time traffic competed for the same resources, creating operational risk and scaling complexity. 2 - 7x Faster analytics queries Snowflake · dbt 
Google Cloud Sample Archived · Sep 9, 2026 Etsy To serve its massive community, Etsy relies on multiple compute engines to process vast amounts of structured and unstructured items and search data. Previously, users read and wrote to Google Cloud Storage using Apache… Accelerated data joining by up to 60% BigQuery · Cloud Storage · Managed Spark 
Databricks Sample Jul 28, 2026 OpenAI OpenAI’s data platform powers one of the fastest-scaling products in tech history. ChatGPT, the OpenAI API, and the internal tools used by thousands of OpenAI employees require a foundation that can withstand sudden… $400K/month Storage costs eliminated by marketing team Databricks Data Warehouses · Databricks Jobs · Databricks SQL